The `neural algorithmic reasoning'[1]} approach presents a procedure for building neural networks that can mimic algorithms. It includes training processor networks that can operate over high-dimensional latent spaces to align with fundamental computations. This improves generalization and reasoning in neural networks.
RAVEN[2]} combines both visual understanding and structural reasoning using a Dynamic Residual tree (DRT) graph developed from structural representation and aggregates latent features in a bottom-up manner. This provides a direction suggesting that augmenting networks with domain knowledge performs better than black-box neural networks. Scattering Compositional learner(SCL)[3]} presents an approach where the model learns a compositional representation by learning independent networks for encoding object, attribute representations and relationship networks for inferring rules, and using their composition to make a prediction. Our work bears similarity with this approach as both utilize background knowledge in composing a larger mechanism from elementary networks. CoPINet[4]} presents the Contrastive Perceptual Inference network which is built on the idea of contrastive learning, i.e. to teach concepts by comparing cases. The Dual-Contrast Network (DCNet)[5]} works on similar lines as it uses 2 contrasting modules: rule contrast and choice contrast for its training. We draw inspiration from [6]} which also presents a variation of the Neural Algorithmic Reasoning approach applied to visual reasoning.
